July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Measurement of functional vision in people with ultra low vision using a virtual reality headset
Author Affiliations & Notes
  • Arathy G Kartha
    Wilmer Eye Institute, Baltimore, Maryland, United States
  • Roksana Sadeghi
    Wilmer Eye Institute, Baltimore, Maryland, United States
    Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
  • Chau Tran
    BaltiVirtual, Baltimore, Maryland, United States
  • Zoe Nardo
    BaltiVirtual, Baltimore, Maryland, United States
  • Olukemi Adeyemo
    Wilmer Eye Institute, Baltimore, Maryland, United States
  • Liancheng Yang
    Wilmer Eye Institute, Baltimore, Maryland, United States
  • Duane Geruschat
    Wilmer Eye Institute, Baltimore, Maryland, United States
  • Gislin Dagnelie
    Wilmer Eye Institute, Baltimore, Maryland, United States
  • Footnotes
    Commercial Relationships   Arathy Kartha, None; Roksana Sadeghi, None; Chau Tran, BaltiVirtual (E); Zoe Nardo, BaltiVirtual (E); Olukemi Adeyemo, None; Liancheng Yang, None; Duane Geruschat, None; Gislin Dagnelie, None
  • Footnotes
    Support  NIH R01EY028452
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 4040. doi:
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    • Get Citation

      Arathy G Kartha, Roksana Sadeghi, Chau Tran, Zoe Nardo, Olukemi Adeyemo, Liancheng Yang, Duane Geruschat, Gislin Dagnelie; Measurement of functional vision in people with ultra low vision using a virtual reality headset. Invest. Ophthalmol. Vis. Sci. 2019;60(9):4040.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : There are limited options for measuring functional vision in people with ultra low vision (ULV). However, people with ULV have some residual vision, and they use it for a range of activities (Adeyemo. TVST 6:10, 2017). It is important to measure the visual potential in these patients to determine appropriate strategies for rehabilitation; moreover, such measures can be used in feasibility studies for novel vision-restoring therapies. The purpose of this study was to develop and evaluate the efficacy of virtual reality based functional vision test for assessment of residual vision in people with ULV.

Methods : Subjects with real or simulated ULV participated in the study. Subjects were presented 17 different scenes, in random order, to test detection, localization, or motion of everyday objects in a virtual reality headset. The scenes were based on real-world activities (Adeyemo, ARVO #4688, 2017) developed from items in the ULV visual functioning questionnaire (Jeter, TVST 6:11, 2017). 13 items were luminance/contrast-based and 4 were motion- based with 3 difficulty settings and 3 trials per condition. Responses were recorded in a 2-, 3-, or 4-alternative forced choice design. The number of correct trials yielded the final score for each item/condition.

Results : We present results from 8 normally sighted subjects, with simulated ULV using translucent eye patches of 2 density levels. Overall, responses were in agreement with the level of item difficulty presented and patch density, as evidenced by the separation in the Guttman’s Scalogram (Fig. 1). Cronbach’s co-efficient of reproducibility was 80.2%. A Friedman test showed that the mean ranks were significantly different with the subjects scoring highest for the items with better contrast and vice versa (χ2(50)= 446.2, p<0.001). There was a significant difference between the 2 impairment levels (p<0.05) for tasks with lower scores; no significant differences within the same impairment level (p=0.08) by non-parametric methods.

Conclusions : The responses obtained from subjects with simulated ULV showed consistent scaling of item difficulties and severity of functional visual impairment. Based on this successful pilot study we are now collecting data in ULV subjects. Rasch analysis and a new d'-based analysis will be used once a larger data set is available.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Fig.1 Guttman Scalogram representing the item scores for patch 1 (top) and patch 2 (bottom).

Fig.1 Guttman Scalogram representing the item scores for patch 1 (top) and patch 2 (bottom).

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